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\newcommand{\TopK}{\operatorname{TopK}}

\title[DoSA]{DoSA: Dots Sparse Attention}
\subtitle{Exact token selection with conditional full scoring}
\author{Paper authors: Fanxu Meng and Taosong Fang\\[2pt]\small Dots Studio, Xiaohongshu Inc.}
\date{Paper discussion \;|\; arXiv:2610.13990v1 \;|\; 19 October 2026}

\begin{document}

% Main presentation: approximately 25 minutes. Frames 23--28 are backups.
\begin{frame}
  \titlepage
  \vspace{-8mm}
  \begin{center}
  \alert{Prove that a key cannot win before computing its full score.}
  \end{center}
  \papersource{Independent reading presentation. \href{https://arxiv.org/abs/2610.13990v1}{arxiv.org/abs/2610.13990v1}.}
\end{frame}

\begin{frame}{Choosing sparse attention can dominate prefill}
  \centering
  \includegraphics[width=.92\linewidth,height=.55\textheight,keepaspectratio]{figures/fig-01-indexer-cost.pdf}
  \begin{block}{At approximately 1M tokens}
    The Indexer accounts for \textbf{73.0\%} of GLM-5.2 prefill GPU time and \textbf{90.8\%} of DeepSeek-V3.2 prefill GPU time.
  \end{block}
  \papersource{Paper, Fig. 1, PDF p. 2; profiling scope: App. D.1, p. 18. Time shares are GPU prefill shares, not TTFT shares.}
\end{frame}

\begin{frame}{The original scorer is a signed sum of ReLU heads}
  \[
  s_i=\sum_{h=1}^{H} w_h\relu{\q_h^\top\kk_i},\qquad i\in\Omega.
  \]
  \begin{itemize}
    \item $\q_h,\kk_i\in\mathbb R^D$; $w_h$ may be positive or negative.
    \item $\Omega$ contains visible keys; return $K$ distinct token IDs.
    \item SGLang scores all heads; LiteTopK reduces traffic through fusion.
    \item DoSA also avoids full scoring for certified losers.
  \end{itemize}
  \begin{block}{The design problem}
    Build a useful upper bound more cheaply than evaluating all $H$ heads.
  \end{block}
  \papersource{Paper, Secs. 1--2.1, PDF pp. 1--2; Eq. (1). The model's original scoring rule is preserved.}
\end{frame}

\begin{frame}{A seed set gives a safe lower bound on the cutoff}
  Score distinct visible seeds $\mathcal S$, with $|\mathcal S|\ge K$:
  \[
    \tau=\tau_K(\mathcal S)\le\tau_K(\Omega)=\tau^*.
  \]
  \begin{block}{Certificate of rejection}
    \centering $s_i\le U_i<\tau\quad\Longrightarrow\quad i\notin\TopK(\Omega)$.
  \end{block}
  \begin{columns}[T,onlytextwidth]
    \begin{column}{.48\textwidth}
      \textbf{Illustrative example: $K=2$}\\
      Seed scores: $10,7,3$; hence $\tau=7$.\\[3pt]
      $U_i=6.5$: reject without full scoring.
    \end{column}
    \begin{column}{.48\textwidth}
      \textbf{Equality must survive}\\
      $U_i=7$: retain and compute $s_i$.\\[3pt]
      Weak seeds reduce efficiency, not safety.
    \end{column}
  \end{columns}
  \papersource{Paper, Eq. (2), p. 2; App. A.2, p. 14. Example is instructional. The prefill scan uses a fixed threshold.}
\end{frame}

\begin{frame}{Screen, compact, fully score, and selectively write}
  \centering
  \includegraphics[width=.94\linewidth,height=.67\textheight,keepaspectratio]{figures/fig-02-pipeline.pdf}
  \par\smallskip
  \alert{Bounds decide eligibility; original scores decide the final ranking.}
  \papersource{Paper, Fig. 2, PDF p. 3; Sec. 2.5, p. 4. Seed initialization and final Top-K are omitted in the original figure.}
\end{frame}

\begin{frame}{Spend direct evaluation on hard-to-bound heads}
  Retain $r$ heads in $R$; omitted positives are $P$, omitted negatives are $N$.
  \begin{block}{If all positive heads fit}
    Retain them all. Fill the remaining slots with negative heads ranked by
    \[
    |w_h|\norm{\q_h}\min(p_h,1-p_h).
    \]
  \end{block}
  \begin{itemize}
    \item $p_h$: activation frequency on sampled seed keys.
    \item Strong heads with uncertain activation are valuable to evaluate.
    \item If positives exceed the budget, retain the $r$ strongest by $w_h\norm{\q_h}$.
  \end{itemize}
  \papersource{Paper, Sec. 2.2, PDF p. 3; surrogate derivation: App. B.2, p. 16. Any retained set is safe; selection affects tightness.}
\end{frame}

\begin{frame}{Positive heads reuse a retained direction plus a residual}
  For an omitted positive head $h$, choose a retained anchor $j$:
  \[
    \beta_h=\frac{\relu{\q_h^\top\q_j}}{\norm{\q_j}^2}\ge0,
    \qquad \mathbf d_h=\q_h-\beta_h\q_j.
  \]
  \begin{block}{A reusable upper bound}
    \[
      \relu{\q_h^\top\kk_i}
      \le\beta_h\relu{\q_j^\top\kk_i}+\norm{\mathbf d_h}\norm{\kk_i}.
    \]
  \end{block}
  \begin{itemize}
    \item Nonnegative projection respects ReLU positive homogeneity.
    \item Fold $w_h\beta_h$ into the anchor weight $\widetilde w_j$.
    \item Sum residual allowances into $E_P=\sum_{h\in P}w_h\norm{\mathbf d_h}$.
  \end{itemize}
  \papersource{Paper, Eqs. (4)--(6), PDF pp. 3--4. Choose the anchor minimizing the residual; zero anchors use coefficient zero.}
\end{frame}

\begin{frame}{Negative aggregation must avoid inactive-head cancellation}
  For any support $A\subseteq N$:
  \[
    \mathbf a_A=\sum_{h\in A}|w_h|\q_h,\qquad
    \relu{\mathbf a_A^\top\kk_i}
    \le\sum_{h\in N}|w_h|\relu{\q_h^\top\kk_i}.
  \]
  \begin{columns}[T,onlytextwidth]
    \begin{column}{.48\textwidth}
      \begin{block}{Example: $w_1=w_2=-1$}
        True subtraction: $[5]_++[-4]_+=5$.\\
        Aggregate both: $[5-4]_+=1$.\\
        Aggregate the active head only: $5$.
      \end{block}
    \end{column}
    \begin{column}{.48\textwidth}
      \begin{block}{Activation-informed support}
        $A=\{h\in N:p_h\ge1/2\}$.\\[4pt]
        Seed statistics improve tightness. Every support remains safe.
      \end{block}
    \end{column}
  \end{columns}
  \papersource{Paper, Sec. 2.4 and Eq. (8), PDF p. 4; App. B.2, pp. 15--16. Numerical example is instructional.}
\end{frame}

\begin{frame}{The certificate preserves every possible Top-K winner}
  \[
  \boxed{U_i=\sum_{j\in R}\widetilde w_j\relu{\q_j^\top\kk_i}
     +E_P\norm{\kk_i}-\relu{\mathbf a_A^\top\kk_i}\ge s_i}
  \]
  \begin{enumerate}
    \item Screen non-seeds: $V=\{i:U_i\ge\tau\}$.
    \item Fully score survivors; keep $C=\{i\in V:s_i\ge\tau\}$.
    \item Select $\TopK(\mathcal S\cup C)$ using original scores.
  \end{enumerate}
  \begin{block}{Why it works}
    Every discarded key has $s_i<\tau\le\tau^*$. No winner is discarded, provided live candidates are never lost.
  \end{block}
  \papersource{Paper, Eq. (9), p. 4; Algorithm 1 and Theorem 1, p. 14. Identical tied IDs require a common total tie order.}
\end{frame}

\begin{frame}{FP8 exactness requires an upper bound on computed scores}
  The implementation must establish $s_i^{\rm ref}\le U_i^{\rm num}$:
  \[
  U_i^{\rm num}=\operatorname{Eval}_{\rm up}\!\left[
    \alpha_i(\widehat U_i+\rho_i C+\varepsilon_{\rm raw})
    +\varepsilon_{\rm out}\right].
  \]
  \begin{itemize}
    \item $s^{\rm ref}$: original scorer's computed score; use it for seeds and survivors.
    \item $\kk_i=\alpha_i\bar{\kk}_i$, $\alpha_i>0$; $\rho_i\sqrt D\ge\norm{\bar{\kk}_i}$ must hold.
    \item $\widehat U_i$: screening result; $C,\varepsilon_{\rm raw},\varepsilon_{\rm out}$ cover representation and arithmetic errors.
    \item Upward enclosure is essential; round-to-nearest alone is insufficient.
    \item Invalid certificates or lost storage capacity require safe recovery.
  \end{itemize}
  \papersource{Paper, App. C, PDF pp. 17--18, especially Eq. (26). A real-arithmetic proof alone does not establish the FP8 guarantee.}
\end{frame}

\begin{frame}{GPU execution couples pruning with less data movement}
  \centering
  \includegraphics[width=.94\linewidth,height=.51\textheight,keepaspectratio]{figures/fig-03-kernel.pdf}
  \begin{itemize}
    \item Group queries for regular Tensor Core screening.
    \item Four-query refinement groups share repeated key loads.
    \item Emit only threshold-qualified scores; resolve boundary codes exactly.
  \end{itemize}
  \papersource{Paper, Sec. 3 and Fig. 3, PDF p. 5. Shared stream: at least two surviving queries and common visibility.}
\end{frame}

\begin{frame}{The evaluation separates Indexer cost from serving latency}
  \begin{center}
  \begin{tabular}{lcc}
    \toprule
    & GLM-5.2 & DeepSeek-V3.2\\
    \midrule
    Indexer heads & 32 & 64\\
    Top-K & 2,048 & 2,048\\
    End-to-end parallelism & TP8 & TP8\\
    End-to-end prefill chunk & 32K & 16K\\
    \bottomrule
  \end{tabular}
  \end{center}
  \begin{itemize}
    \item English/Chinese books and code; 128K to approximately 1M tokens.
    \item Baselines: SGLang v0.5.18 and LiteTopK.
    \item Complete-Indexer: GLM, one GPU, all 21 Indexer-producing layers, including query/key preparation.
    \item TTFT: individual requests; prefix cache cleared between requests.
  \end{itemize}
  \papersource{Paper, Sec. 4.1, PDF pp. 6--7; App. D.1, p. 18. GPU vendor is stated, but the specific GPU model is not reported.}
\end{frame}

\begin{frame}{DoSA wins all 60 complete-Indexer configurations}
  \begin{columns}[c,onlytextwidth]
    \begin{column}{.67\textwidth}
      \includegraphics[width=\linewidth,height=.73\textheight,keepaspectratio]{figures/fig-04-prefill.pdf}
    \end{column}
    \begin{column}{.30\textwidth}
      \textbf{Overall speedup}\\[3pt]
      SGLang: 1.12--2.74$\times$\\
      LiteTopK: 1.17--1.69$\times$\\[10pt]
      \textbf{At approximately 1M}\\[3pt]
      SGLang: 2.47--2.74$\times$\\
      LiteTopK: 1.47--1.69$\times$\\[8pt]
      Gains grow mainly with context length.
    \end{column}
  \end{columns}
  \papersource{Paper, Fig. 4, p. 6; Sec. 4.2, p. 7. 3 domains $\times$ 5 lengths $\times$ 4 chunk sizes; plot normalized to SGLang.}
\end{frame}

\begin{frame}{Most pairs are rejected, but screening still costs work}
  \begin{columns}[c,onlytextwidth]
    \begin{column}{.54\textwidth}
      \includegraphics[width=\linewidth,height=.65\textheight,keepaspectratio]{figures/fig-06-pruning.pdf}
    \end{column}
    \begin{column}{.43\textwidth}
      \begin{itemize}
        \item Mean pruning: 97.2--97.5\% by domain.
        \item 20 of 21 layers exceed 95\% in each domain.
        \item Layer 1: approximately 89--93\%.
        \item The rate counts query--key pairs avoiding full scoring.
      \end{itemize}
    \end{column}
  \end{columns}
  \begin{block}{Interpretation}
    High rejection does not eliminate the retained-head scan or key reads.
  \end{block}
  \papersource{Paper, Fig. 6, p. 7; Sec. 4.2, pp. 7--8. Cost model: App. B.3, p. 16.}
\end{frame}

\begin{frame}{At approximately 1M tokens, TTFT is nearly halved}
  \begin{columns}[c,onlytextwidth]
    \begin{column}{.49\textwidth}
      \centering\textbf{GLM-5.2}\\
      \includegraphics[width=\linewidth,height=.40\textheight,keepaspectratio]{figures/fig-08a-ttft-glm.pdf}
    \end{column}
    \begin{column}{.49\textwidth}
      \centering\textbf{DeepSeek-V3.2}\\
      \includegraphics[width=\linewidth,height=.40\textheight,keepaspectratio]{figures/fig-08b-ttft-deepseek.pdf}
    \end{column}
  \end{columns}
  \begin{center}
  \begin{tabular}{lcc}
    \toprule
    Model (approximately 1M tokens) & vs. SGLang & vs. LiteTopK\\
    \midrule
    GLM-5.2 & 1.86$\times$ & 1.26$\times$\\
    DeepSeek-V3.2 & 1.94$\times$ & 1.50$\times$\\
    \bottomrule
  \end{tabular}
  \end{center}
  \papersource{Paper, Sec. 4.3 and Fig. 8, PDF p. 8. TP8; chunks 32K / 16K. Panel y-scales differ; lower TTFT is better.}
\end{frame}

\begin{frame}{Screening and sparse writeback remove different costs}
  \begin{center}
  \begin{tabular}{lcccc}
    \toprule
    Variant & Screening & Sparse writes & Time (ms) & Reduction\\
    \midrule
    Base & No & No & 76.2 & ---\\
    Full & No & Yes & 58.4 & 23.4\%\\
    Dense & Yes & No & 47.7 & 37.4\%\\
    DoSA & Yes & Yes & 40.2 & 47.3\%\\
    \bottomrule
  \end{tabular}
  \end{center}
  \begin{itemize}
    \item LOFT SciFact example: 16K queries, 896K keys; mean over 21 layers.
    \item Full still computes every head; Dense still pays for dense writeback.
    \item Together: 1.90$\times$ retrieval speedup over Base.
  \end{itemize}
  \papersource{Paper, Fig. 7, p. 7; Sec. 4.4, p. 8. Times reproduced from figure labels; reductions from text.}
\end{frame}

\begin{frame}{Negative aggregation is decisive in the measured workload}
  \centering
  \includegraphics[width=.87\linewidth,height=.52\textheight,keepaspectratio]{figures/fig-09-components.pdf}
  \begin{itemize}
    \item Removing it raises survival from 2.65\% to 95.06\%.
    \item The 9.507$\times$ latency includes candidate-pool recovery.
    \item Positive projection is needed in only 0.42\% of query-layer rows.
  \end{itemize}
  \papersource{Paper, App. D.2 and Fig. 9, PDF pp. 18--19. Mean survival; geometric mean time ratio. Breaks mark truncated bars.}
\end{frame}

\begin{frame}{Decoding uses full scoring and a smaller selection problem}
  \begin{columns}[c,onlytextwidth]
    \begin{column}{.35\textwidth}
      \includegraphics[width=\linewidth,height=.62\textheight,keepaspectratio]{figures/fig-05-decode.pdf}
    \end{column}
    \begin{column}{.61\textwidth}
      \begin{enumerate}
        \item Re-score recent, distinct winner IDs for the current query.
        \item Use them as witnesses for a conservative cutoff.
        \item Fully score keys; compact qualifying records for exact selection.
      \end{enumerate}
      \medskip
      Equal-length requests fill approximately 1M total cached tokens.
    \end{column}
  \end{columns}
  \papersource{Paper, Sec. 3, p. 5; Fig. 5 and Sec. 4.2, p. 7. Reported gains are decoding Indexer gains, not end-to-end token latency.}
\end{frame}

\begin{frame}{Margins and head budgets determine whether screening pays}
  Define $\Delta_i=\tau^*-s_i$, $\delta_i=U_i-s_i$, and $b=\tau^*-\tau$:
  \[
    \boxed{U_i<\tau\quad\Longleftrightarrow\quad\Delta_i>\delta_i+b.}
  \]
  \begin{itemize}
    \item Better seeds reduce $b$; better geometry reduces $\delta_i$.
    \item Many near-boundary keys can survive even a valid certificate.
  \end{itemize}
  \[
    \frac{\text{DoSA dot products}}{\text{baseline dot products}}
    \approx f+(1-f)\left(\frac{r+d_a}{H}+m\right).
  \]
  $f$: initialization fraction; $m$: survivor fraction; $d_a$: auxiliary rows.
  \papersource{Paper, App. B.3, Eqs. (17)--(20), PDF p. 16. Logical-work model excludes planning, padding, selection, and recovery.}
\end{frame}

\begin{frame}{Layer-level gains come with a material storage qualification}
  \begin{center}
  \begin{tabular}{lcc}
    \toprule
    Retrieval-only comparison & Faster cells & Peak speedup\\
    \midrule
    vs. SGLang & 251/252 & 3.74$\times$\\
    vs. LiteTopK & 250/252 & 2.08$\times$\\
    \bottomrule
  \end{tabular}
  \end{center}
  \begin{itemize}
    \item Prepared queries/keys; 32K queries; 12 replays per method and cell.
    \item Five cells remeasured after a candidate-capacity repair; 247 retained.
    \item Repair adds \textbf{9.94 GiB} of workspace at approximately 1M-key capacity.
    \item The full matrix was not rerun; three slower comparisons remain visible.
  \end{itemize}
  \papersource{Paper, App. D.4, PDF p. 20; Figs. 11--12, p. 21. This scope differs from complete-Indexer benchmarks.}
\end{frame}

\begin{frame}{The contribution is exact conditional scoring inside an Indexer}
  \begin{block}{What the evidence supports}
    A signed-head certificate can skip full scoring, and an aligned GPU pipeline turns that into substantial long-context prefill gains.
  \end{block}
  \begin{itemize}
    \item Every key is still scanned; short contexts or loose bounds can erase gains.
    \item Numerical dominance, storage recovery, and tie handling are essential.
    \item Beyond-context measurements establish computational scaling, not task accuracy.
    \item Next steps: slack-aware training and certificates for whole key blocks.
  \end{itemize}
  \papersource{Paper, Sec. 6, PDF p. 9; App. C and D.1, pp. 17--18. Future directions are proposals, not measured results.}
\end{frame}

\begin{frame}{Paper and discussion prompts}
  \textbf{Fanxu Meng and Taosong Fang.}\\
  \emph{DoSA: Dots Sparse Attention.} arXiv:2610.13990v1.\\[5pt]
  \href{https://arxiv.org/abs/2610.13990v1}{https://arxiv.org/abs/2610.13990v1}\\
  Author-linked repository: \href{https://github.com/llm-sys/flash-indexer}{github.com/llm-sys/flash-indexer}
  \medskip
  \begin{itemize}
    \item Can training improve both selection quality and certificate tightness?
    \item Can a block bound avoid key reads without becoming too loose?
    \item How should head budgets trade off arithmetic, memory, and fallback cost?
  \end{itemize}
  \papersource{Figures are unmodified author assets. Instructional examples and discussion prompts are this presentation's analysis.}
\end{frame}

\appendix

\begin{frame}{Backup: joint residuals exploit a shared key direction}
  Let $\mathbf u_h=w_h\mathbf d_h$ for omitted positive heads:
  \[
    E_P^{\rm joint}=\frac12\left(
       \norm{\sum_h\mathbf u_h}
       +\sqrt{\sum_{h,\ell}|\mathbf u_h^\top\mathbf u_\ell|}
    \right)\le\sum_h\norm{\mathbf u_h}=E_P.
  \]
  \begin{itemize}
    \item Use $[x]_+=(x+|x|)/2$, then bound signed sums via the Gram matrix.
    \item Opposite residuals $\mathbf u,-\mathbf u$: allowance falls from $2\norm{\mathbf u}$ to $\norm{\mathbf u}$.
    \item Coordinate groups combine as $\sqrt{\sum_g B_g^2}\norm{\kk}$.
    \item No extra key summary; query-side cost is quadratic in $|P|$.
  \end{itemize}
  \papersource{Paper, Eq. (7), p. 4; App. A.1, pp. 13--14. Opposite-direction example is instructional.}
\end{frame}

\begin{frame}{Backup: negative slack explains majority support}
  With $x_h=\q_h^\top\kk$, $c_h=|w_h|$, $Z_A^\pm=\sum_{h\in A}c_h[\pm x_h]_+$:
  \[
    U-s=\underbrace{\delta_P}_{\text{positive allowance}}
       +\underbrace{\sum_{h\in N\setminus A}c_h[x_h]_+}_{\text{missed active negatives}}
       +\underbrace{\min(Z_A^+,Z_A^-)}_{\text{cancellation}}.
  \]
  A bound on mean normalized slack (nonzero sample keys):
  \[
    J(A)=\sum_{h\in A}a_h(1-p_h)+\sum_{h\in N\setminus A}a_hp_h,
    \quad a_h=|w_h|\norm{\q_h}.
  \]
  \begin{itemize}
    \item Independent minimization gives $p_h\ge1/2$.
    \item Retaining a negative head removes its $a_h\min(p_h,1-p_h)$ term.
    \item This minimizes a surrogate, not runtime or survivor count directly.
  \end{itemize}
  \papersource{Paper, App. B.2, Eqs. (14)--(16), PDF pp. 15--16. Displayed slack decomposition uses the triangle allowance.}
\end{frame}

\begin{frame}{Backup: the 32-head kernel also uses an absolute-value form}
  If all positive heads are retained, define
  \[
    \mathbf V=\mathbf a_A-\tfrac12\sum_{j\in R}w_j\q_j.
  \]
  Then
  \[
  \begin{aligned}
    U_{\rm abs}(\kk)
      &=\tfrac12\sum_{j\in R}w_j|\q_j^\top\kk|-\mathbf V^\top\kk\\
      &=S_R(\kk)-\mathbf a_A^\top\kk\ge s(\kk).
  \end{aligned}
  \]
  \begin{itemize}
    \item Additional slack versus the base certificate: $[-\mathbf a_A^\top\kk]_+$.
    \item 32-head paths: $r=14,16$ use this form; $r=20$ uses the base form.
    \item Two FP8 auxiliary rows encode $\mathbf V$; both forms cost $r+2$ dots.
  \end{itemize}
  \papersource{Paper, App. C.1, Eqs. (24)--(25), PDF p. 17. These budgets are specified for the 32-head implementation.}
\end{frame}

\begin{frame}{Backup: the RMS correction must enclose all relevant errors}
  A sufficient error contract is
  \[
  s_i^{\rm ref}\le\alpha_i\!\left(
   \widehat U_i+\sum_gE_g^+\norm{\bar{\kk}_i^{(g)}}
   +\sum_dJ_d|\bar k_{i,d}|+\varepsilon_{\rm raw}\right)
   +\varepsilon_{\rm out}.
  \]
  It can be covered with a conservative coefficient
  \[
    C\ge\sqrt D\left(\sum_g[E_g^++\norm{\mathbf J^{(g)}}]^2\right)^{1/2}.
  \]
  \begin{itemize}
    \item $E_g^+$ encloses positive residuals; $\mathbf J$ covers coordinatewise errors.
    \item Include screening and reference-scorer arithmetic, not just quantization.
    \item Conservative RMS, coefficients, scaling, and final evaluation are all needed.
  \end{itemize}
  \papersource{Paper, App. C.2, Eqs. (27)--(28), PDF pp. 17--18. Absolute-value paths use directed FP64 intervals in planning.}
\end{frame}

\begin{frame}{Backup: candidate capacity and ties are correctness conditions}
  \begin{block}{Top-K output size does not bound the live candidate set}
    If every score equals $\tau$, every candidate survives, even with exact bounds.
  \end{block}
  \begin{itemize}
    \item Full buffers: extend, process with exact Top-K merging, or recover globally.
    \item Never truncate live IDs or recover only from an incomplete buffer.
    \item Query-level numerical errors require a full visible-domain recovery, including previously discarded keys.
    \item Keep equality at both filters. Match total tie order for identical tied IDs.
    \item Different valid tied sets may produce different generated text.
  \end{itemize}
  \papersource{Paper, Theorem 1, p. 14; App. C.3, p. 18; Sec. 6, p. 9. Finite-score theorem does not define NaN semantics.}
\end{frame}

\begin{frame}{Backup: all-negative support is especially costly in early layers}
  \centering
  \includegraphics[width=.90\linewidth,height=.61\textheight,keepaspectratio]{figures/fig-10-layer-components.pdf}
  \par\smallskip
  All-negative support reaches 3.41$\times$ relative Indexer latency at layer 2.
  \papersource{Paper, App. D.3, pp. 19--20; Fig. 10, p. 20. Both vertical axes are logarithmic; same samples as component ablation.}
\end{frame}

\end{document}